%load_ext autoreload
%autoreload 2
Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of this structure is that images can be described by a relatively sparse number of features. We designed a sparse coding algorithm biologically-inspired by the architecture of the primary visual cortex. We show here that coefficients of this representation exhibit a power-law distribution. The exponent of this distribution characterizes sparseness and varies from image to image. To investigate the role of this sparseness, we designed a new class of random textured stimuli with a controlled sparseness value inspired by measurements of natural images. Then, we provide with a method to modify the sparseness statistics observed in any image to match that of some class of natural images and provide perspectives for their use in neurophysiology.
%run -i -t -N1 EUVIP_1_defaults.ipynb
%run -i -t -N1 EUVIP_2_lena.ipynb
%run -i -t -N1 EUVIP_3-sparsecoding.ipynb
%run -i -t -N1 EUVIP_4-statistics_natural_images.ipynb
%run -i -t -N1 EUVIP_5-activities-fits.ipynb
%run -i -t -N1 EUVIP_6-droplets.ipynb
%load_ext watermark
%watermark
%load_ext version_information
%version_information numpy, scipy, matplotlib, sympy